Search: “safety”

Formalizing Task-Space Complexity for Zero-Shot Generalization

arXiv:2606.20967v1 Announce Type: new Abstract: Policies must operate across diverse conditions, yet a single policy is often conservative while fully adaptive schemes can be complex. We study zero-sh

From Gradient Clipping to Structural Refinement: Improving DPSGD for Medical Image Segmentation

DGX agent

arXiv:2606.21763v1 Announce Type: new Abstract: Medical image segmentation is widely used for disease detection but relies on sensitive data, raising privacy concerns as trained models can leak inform

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23 Jun 2026

From Reconstruction to Decision: A Post-Encoder Plug-in Adapter for Curvilinear Segmentation

arXiv:2606.23486v1 Announce Type: new Abstract: Curvilinear object segmentation, including vessels and cracks, is challenging due to extreme spatial sparsity and topological fragility, where small loc

FTP-1: A Generalist Foundation Tactile Policy Across Tactile Sensors for Contact-Rich Manipulation

arXiv:2606.13102v2 Announce Type: replace Abstract: Despite the success of vision-based generalist robotic policies, existing tactile-based policies remain tied to fixed embodiments and sensor setups.

GAC: Stabilizing Asynchronous RL Training for LLMs via Gradient Alignment Control

arXiv:2603.01501v2 Announce Type: replace Abstract: Asynchronous execution is essential for scaling reinforcement learning (RL) to modern large model workloads, including large language models and AI

GARIP: A Running-Average Moving Reference for Last-Iterate Self-Play in Two-Player Zero-Sum Games

arXiv:2606.22688v1 Announce Type: cross Abstract: Self-play with naive gradient ascent cycles in two-player zero-sum games: the last iterate orbits the equilibrium. Modern methods restore last-iterate

Gaussian process policy iteration with additive Schwarz acceleration for forward and inverse HJB and mean field game problems

arXiv:2505.00909v3 Announce Type: replace Abstract: In this paper, we propose a Gaussian Process (GP)-based policy iteration framework for addressing both forward and inverse problems in Hamilton--Jac

Geometric Entropy: When Trajectory Diversity Helps and Hurts in Imitation Learning

arXiv:2606.20871v1 Announce Type: new Abstract: We study how trajectory-shape diversity in demonstrations affects imitation learning (IL) performance across models, tasks, and data scales. We introduc

Graph Alignment via Dual-Pass Spectral Encoding and Latent Space Communication

arXiv:2509.09597v3 Announce Type: replace-cross Abstract: Graph alignment, the problem of identifying corresponding nodes across multiple graphs, is fundamental to numerous applications. Most existing

Graph-of-Differences: Anatomy-Structured Difference Alignment for Medical Image Re-Identification

arXiv:2606.21368v1 Announce Type: new Abstract: Medical image re-identification (MedReID) enables longitudinal patient linkage but remains vulnerable to shortcut learning and often produces decisions

Group-Graph Policy Optimization for Long-Horizon Agentic Reinforcement Learning

arXiv:2606.22995v1 Announce Type: new Abstract: Group-based Reinforcement Learning (RL) has significantly enhanced Large Language Models (LLMs) in agentic scenarios. To achieve finer-grained policy up

GTA-Net: Cooperative Game Theory for Vision-Language Alignment in Chest X-Ray Report Generation

arXiv:2606.21915v1 Announce Type: new Abstract: Automated chest X-ray report generation requires precise cross-modal grounding to ensure clinically reliable descriptions. However, existing vision-lang

Happy Young Women, Grumpy Old Men? Emotion-Driven Demographic Biases in Synthetic Face Generation

arXiv:2602.00032v3 Announce Type: replace-cross Abstract: Synthetic faces from text-to-image (T2I) models pervade digital media, yet their demographic biases under emotionally conditioned prompts rema

HEAS: Hierarchical Evolutionary Agent-Based Simulation Framework for Multi-Objective Policy Search

arXiv:2508.15555v4 Announce Type: replace-cross Abstract: HEAS is a Python framework that connects agent-based simulation, evolutionary search, and scenario-based evaluation in a single reproducible p

Heterogeneous Policy Networks for Composite Robot Team Communication and Coordination

arXiv:2606.20962v1 Announce Type: new Abstract: High-performing human-human teams learn intelligent and efficient communication and coordination strategies to maximize their joint utility. These teams

Hierarchical Reinforcement Learning for Sparse-Reward Search in Commutative Algebra

arXiv:2606.22922v1 Announce Type: new Abstract: Applying machine learning techniques to solving long-standing mathematical conjectures can be particularly challenging due to their extreme reward spars

HiL-ResRL: A Model-Agnostic Finetuning Adapter via Human-in-the-loop Residual Reinforcement Learning

arXiv:2606.22860v1 Announce Type: new Abstract: Recent advancements in generative imitation learning have significantly propelled the field of robotic manipulation. However, the majority of existing m

HilDA: Hierarchical Distillation with Diffusion for Advancing Self-Supervised LiDAR Pre-training

arXiv:2606.20189v2 Announce Type: replace Abstract: Leveraging Vision Foundation Models (VFMs) for camera-to-LiDAR knowledge distillation offers a promising solution to the scarcity of annotated data

Horizon Adaptive Offline Policy Learning via Value Stitching

arXiv:2606.21136v1 Announce Type: new Abstract: Learning accurate value functions plays a decisive role for reinforcement learning (RL) agents to solve long-horizon, complex tasks. Conventional tempor

Imagine2Act: Leveraging Object-Action Motion Consistency from Imagined Goals for Robotic Manipulation

arXiv:2509.17125v2 Announce Type: replace Abstract: Relational object rearrangement (ROR) tasks (e.g., insert flower to vase) require a robot to manipulate objects with precise semantic and geometric

Imitation from Heterogeneous Demonstrations using Grounded Latent-Action World Models

arXiv:2606.21672v1 Announce Type: cross Abstract: Imitation learning has emerged as a powerful paradigm for learning visuomotor policies, but its generalisation and stability are limited by the scale

Improved Algorithms for Nash Welfare in Linear Bandits

arXiv:2601.22969v2 Announce Type: replace Abstract: Nash regret has recently emerged as a principled fairness-aware performance metric for stochastic multi-armed bandits, motivated by the Nash Social

Improving Reasoning in Vision-Language Models via Perception Verified Self-Training

arXiv:2606.22158v1 Announce Type: new Abstract: Achieving human-like reasoning in Vision-Language Models (VLMs) remains a long-standing challenge. Recent approaches leverage Chain-of-Thought (CoT) rat

Improving Robotic Imitation Learning via Trajectory Standardization

arXiv:2606.22907v1 Announce Type: cross Abstract: Imitation learning for robotic manipulation relies on large sets of human demonstration trajectories, which are often noisy and temporally irregular d

Inductive Generalization for Robotic Manipulation

arXiv:2606.20999v1 Announce Type: cross Abstract: Understanding the generalization capabilities of visuomotor policies is essential in the development of capable robotic agents. Generalizable models l

Influencer Cartels

arXiv:2405.10231v3 Announce Type: replace-cross Abstract: Social media influencers account for a growing share of marketing worldwide. We demonstrate the existence of a novel form of market failure in

'Instead of allowing for greater focus, the latest AI tools are overwhelming workers, frazzling minds and shredding attention spans.' https:…

'Instead of allowing for greater focus, the latest AI tools are overwhelming workers, frazzling minds and shredding attention spans.' https://www.theatlantic.com/technology/2026/06/ai-agents-jobs-exha

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Integrating Facial Generation into Full-Duplex Spoken Dialogue Systems

arXiv:2606.21970v1 Announce Type: cross Abstract: Full-duplex spoken dialogue models, such as Moshi, enable natural, low-latency voice conversations. However, they remain limited to the audio modality

Interpretable Probabilistic Medical Image Segmentation via Gaussian Process with Explicit Modelling of Annotation Bias and Variability

arXiv:2606.23177v1 Announce Type: new Abstract: Deep learning-based medical image segmentation models are trained using annotations that exhibit systematic bias and variability across raters. While pr

Inverting the Bellman Equation: From Q-Values to World Models

arXiv:2606.21173v1 Announce Type: new Abstract: Model-based and model-free reinforcement learning are traditionally viewed as separate paradigms: instead of learning a model of the transition kernel P

IRumAI: Reinforcement Learning for Indian Rummy

arXiv:2606.21975v1 Announce Type: cross Abstract: Despite its massive player base and complex hidden-information dynamics, Indian Rummy has received no reinforcement learning attention. Existing agent

IViT: A Novel Interpretable Visual Transformer for Skin Disease Detection

arXiv:2606.22892v1 Announce Type: cross Abstract: The clinical diagnosis of skin diseases is susceptible to interference from inter-class similarity of skin lesions, and over-reliance on clinicians'ex

KITE: Decoupling Kinematics and Interaction for Zero-Shot Cross-Embodiment Manipulation

arXiv:2606.22113v1 Announce Type: new Abstract: Generalizing manipulation policies across robot embodiments remains difficult because standard policies entangle task reasoning with embodiment-specific

LaST-HD: Learning Latent Physical Reasoning from Scalable Human Data for Robot Manipulation

arXiv:2606.23685v1 Announce Type: new Abstract: Human-hand demonstrations provide a direct and scalable source of physical interaction data for robot learning. While manual retargeting is indispensabl

Learned Controllers for Agile Quadrotors in Pursuit-Evasion Games

arXiv:2506.02849v3 Announce Type: replace-cross Abstract: In this letter we study 1v1 quadrotor pursuit-evasion, where a pursuer and an evader are trained via reinforcement learning (RL) by competing

Learning Control as Enabling Layer for Embodied Intelligence Research explored with Soft Robotic Swimming in diverse Flow Speeds

arXiv:2606.20660v1 Announce Type: new Abstract: Soft robots are valuable robophysical platforms for studying body-caudal undulatory locomotion, but their compliant bodies are difficult to control prec

Learning Process Rewards via Success Visitation Matching for Efficient RL

arXiv:2606.23640v1 Announce Type: new Abstract: In many modern applications of reinforcement learning (RL), the natural reward for a task of interest is inherently sparse: a reward of 0 is given every

Learning to Place Guards by Reinforcement: A Geo-Free Neural Policy for the Vertex-Guard Art Gallery Problem

arXiv:2606.21604v1 Announce Type: new Abstract: Neural combinatorial optimization (NCO) has shown that policies trained by reinforcement can construct strong solutions to NP-hard problems directly fro

Learning to See While Learning to Act: Diffusion Models for Active Perception in Robot Imitation

arXiv:2606.23625v1 Announce Type: new Abstract: Most imitation learning methods assume full observability in table-top settings. In practice, objects are often occluded, requiring robots to both searc

LOLLA: Deep Reinforcement Learning for Closed-Loop Link Adaptation Towards a GPU-Accelerated AI-RAN

arXiv:2606.23110v1 Announce Type: cross Abstract: Outer-loop link adaptation (OLLA) is widely deployed in 5G NR to track channel variations, yet its reliance on first-order, single-bit feedback degrad

Long-Distance Real-World Navigation of the Legged-Wheeled Robot Go2-W Using Deep Reinforcement Learning

arXiv:2606.21387v1 Announce Type: new Abstract: Legged-wheeled robots have long been studied for their potential to combine the efficient flat-ground mobility of wheels with the rough-terrain capabili

MAGNIFIED: RL Fine-tuning of Multimodal Large Language Models for Motion Planning

arXiv:2606.20641v1 Announce Type: cross Abstract: Multi-modal Large Language Models (MLLMs) have demonstrated remarkable capabilities in semantic understanding and common sense reasoning, making them

Maintain Plasticity in Long-timescale Continual Test-time Adaptation

arXiv:2412.20034v2 Announce Type: replace Abstract: Continual test-time domain adaptation (CTTA) aims to adjust pre-trained source models to perform well over time across non-stationary target environ

MAPS: Multi-Anchor Projection Similarity for Joint Vision-Language Geo-Localization

arXiv:2606.22543v1 Announce Type: new Abstract: Humans localize places by integrating perceptual cues from vision with semantic reasoning from language, forming a scene understanding that is both intu

Measuring Model-Induced Discrimination via Efficient Fairness Approximation

arXiv:2405.09251v2 Announce Type: replace Abstract: Providing various machine learning (ML) applications in the real world, concerns about discrimination hidden in ML models are growing, particularly

Memory Contagion: Cross-Temporal Propagation of Evaluator Bias via Agent Memory

arXiv:2606.23195v1 Announce Type: new Abstract: Large Language Model (LLM) agents increasingly rely on memory systems to maintain long-term coherence. Recent work shows that agent memories degrade dur

MemoryVAM: Integrating Memory into Video Action Model for Robot Manipulation

arXiv:2606.20679v1 Announce Type: cross Abstract: Video-world-model policies learn action-relevant representations by predicting future observations. However, they condition on only a short observatio

Meta copied slot machines to addict kids to Instagram. Now Zuckerberg is turning his company into a prediction market. Meta’s business model…

Meta copied slot machines to addict kids to Instagram. Now Zuckerberg is turning his company into a prediction market. Meta’s business model is profiting from addiction—kids, gamblers, & more. Stop it

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